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Flexible expansion planning of distribution system integrating multiple renewable energy sources: An approximate dynamic programming approach

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  • Sun, Qirun
  • Wu, Zhi
  • Gu, Wei
  • Zhu, Tao
  • Zhong, Lei
  • Gao, Ting

Abstract

Distribution system expansion planning is a long-term process with sequential multistage uncertain factors, so flexible planning methods are required to deal with the various potential investment risks. This paper presents an approximate dynamic programming based flexible distribution system expansion planning model, in which the long-term system load growth uncertainty and short-term power fluctuation are both considered. A flexible investment strategy based on Markov decision process is developed, in which the planning decisions are made sequentially with consideration of both the current costs and future variable costs under long-term load growth uncertainty. This problem is formulated as a large-scale multistage stochastic programming model, which is intractable due to the “curse of dimensionality”. Thus, an approximate dynamic programming approach is used to decompose the original multistage optimization problem into sequential subproblems that can be easily solved. Case studies are carried out on a 24-node distribution system with three planning stages and a 54-node distribution system with five planning stages. Numerical results validate the feasibility and benefits of the proposed planning approach in obtaining a flexible expansion scheme, which can significantly reduce the investment risk and configure renewable energy equipment more reasonably.

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  • Sun, Qirun & Wu, Zhi & Gu, Wei & Zhu, Tao & Zhong, Lei & Gao, Ting, 2021. "Flexible expansion planning of distribution system integrating multiple renewable energy sources: An approximate dynamic programming approach," Energy, Elsevier, vol. 226(C).
    • Handle: RePEc:eee:energy:v:226:y:2021:i:c:s0360544221006162
    DOI: 10.1016/j.energy.2021.120367
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    Cited by:

    1. Lu, Yu & Xiang, Yue & Huang, Yuan & Yu, Bin & Weng, Liguo & Liu, Junyong, 2023. "Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load," Energy, Elsevier, vol. 271(C).
    2. Zhu, Junpeng & Huang, Yong & Lu, Shuai & Shen, Mengya & Yuan, Yue, 2024. "Incorporating local uncertainty management into distribution system planning: An adaptive robust optimization approach," Applied Energy, Elsevier, vol. 363(C).
    3. Sun, Qirun & Wu, Zhi & Ma, Zhoujun & Gu, Wei & Zhang, Xiao-Ping & Lu, Yuping & Liu, Pengxiang, 2022. "Resilience enhancement strategy for multi-energy systems considering multi-stage recovery process and multi-energy coordination," Energy, Elsevier, vol. 241(C).
    4. Lin, Zhiyi & Song, Chunyue & Zhao, Jun & Yin, Huan, 2022. "Improved approximate dynamic programming for real-time economic dispatch of integrated microgrids," Energy, Elsevier, vol. 255(C).
    5. Meng, Fanyi & Bai, Yang & Jin, Jingliang, 2021. "An advanced real-time dispatching strategy for a distributed energy system based on the reinforcement learning algorithm," Renewable Energy, Elsevier, vol. 178(C), pages 13-24.
    6. Xiang, Yue & Dai, Jiakun & Xue, Ping & Liu, Junyong, 2023. "Autonomous topology planning for distribution network expansion: A learning-based decoupled optimization method," Applied Energy, Elsevier, vol. 348(C).
    7. Artis, Reza & Shivaie, Mojtaba & Weinsier, Philip D., 2024. "A flexible urban load density-dependent framework for low-carbon distribution expansion planning in the presence of hybrid hydrogen/battery/wind/solar energy systems," Applied Energy, Elsevier, vol. 364(C).
    8. Zhang, Houwang & Wu, Qiuwei & Chen, Jian & Lu, Lina & Zhang, Jiangfeng & Zhang, Shuyi, 2023. "Multiple stage stochastic planning of integrated electricity and gas system based on distributed approximate dynamic programming," Energy, Elsevier, vol. 270(C).

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